Building Cyber-Resilient Smart Grids with Digital Twins and Data Spaces

The growing interest in Digital Twin (DT) Technology represents a significant advancement in academic research and industrial applications. Leveraging advancements in Internet of Things (IoT), sensors, and communication devices, DTs are increasingly utilised across different sectors, notably in the energy domain such as Power Systems and Smart Grids. DT concepts facilitate the creation of virtual models mirroring physical assets, streamlining real-time data management and analysis. Driven by the potential of DTs to revolutionise energy systems, this paper offers a comprehensive review of DT applications in the power sector, specifically within next-generation energy systems like Smart Grids. TThe integration of DT technology with Machine Learning (ML) algorithms is highlighted as a key factor in significantly enhancing the performance and capabilities of these advanced energy systems. In contrast to prior reviews, our study meticulously investigates all of the crucial components of energy systems, including forecasting, anomaly detection, and security, which are fundamental for improving the management of operational grids. In addition, the study examines the seamless incorporation of Renewable Energy into current grids and investigates how DT technology could contribute to Electric Vehicles for increased sustainability and reliability within the Smart Grid framework. This review underlines that DTs significantly enhance the management of real-time data and analysis, consequently improving operational grid management. There are ample opportunities into further research and development to design a more advanced and digital system as compared to conventional power systems. The findings are presented in clear and concise tables, highlighting current limitations, proposing effective solutions, and identifying potential future research directions in academia and industry.

Smart grids have emerged as a transformative technology in the power sector, enabling efficient energy management. However, the increased reliance on digital technologies also exposes smart grids to various cybersecurity threats and attacks. This article provides a comprehensive exploration of cyberattacks and cybersecurity in smart grids, focusing on critical components and applications. It examines various cyberattack types and their implications on smart grids, backed by real-world case studies and quantitative models. To select optimal cybersecurity options, the study proposes a multi-criteria decision-making (MCDM) approach using the analytical hierarchy process (AHP). Additionally, the integration of artificial intelligence (AI) techniques in smart-grid security is examined, highlighting the potential benefits and challenges. Overall, the findings suggest that “security effectiveness” holds the highest importance, followed by “cost-effectiveness”, “scalability”, and “Integration and compatibility”, while other criteria (i.e., “performance impact”, “manageability and usability”, “compliance and regulatory requirements”, “resilience and redundancy”, “vendor support and collaboration”, and “future readiness”) contribute to the evaluation but have relatively lower weights. Alternatives such as “access control and authentication” and “security information and event management” with high weighted sums are crucial for enhancing cybersecurity in smart grids, while alternatives such as “compliance and regulatory requirements” and “encryption” have lower weighted sums but still provide value in their respective criteria. We also find that “deep learning” emerges as the most effective AI technique for enhancing cybersecurity in smart grids, followed by “hybrid approaches”, “Bayesian networks”, “swarm intelligence”, and “machine learning”, while “fuzzy logic”, “natural language processing”, “expert systems”, and “genetic algorithms” exhibit lower effectiveness in addressing smart-grid cybersecurity. The article discusses the benefits and drawbacks of MCDM-AHP, proposes enhancements for its use in smart-grid cybersecurity, and suggests exploring alternative MCDM techniques for evaluating security options in smart grids. The approach aids decision-makers in the smart-grid field to make informed cybersecurity choices and optimize resource allocation.

Digital Twin technology is rapidly emerging as a transformative force in various industries, playing a pivotal role in the advancement of Industry 4.0. As industries evolve, the implementation of Digital Twin is anticipated to drive significant technological progress across sectors such as manufacturing, smart cities, healthcare, and transportation. This paper presents a layered Digital Twin framework that synthesizes recent advances in sensing, data analytics, and machine learning to enable real-time monitoring, analysis, and predictive capabilities for modern power systems. By leveraging these advanced sensors, the Digital Twin framework enables more accurate system modeling, enhanced situational awareness, and improved decision-making processes. This approach aims to significantly transform traditional smart grid operations by offering a more responsive, resilient, and efficient energy management system, ultimately contributing to the optimization and modernization of existing power infrastructure. Furthermore, by highlighting key smart grid applications, the paper demonstrates the effectiveness of Digital Twin and sensor technology in optimizing power distribution, reducing downtime, and addressing prevalent energy challenges, thereby paving the way for resilient and intelligent power systems.

The relevance of the study is due to the need for effective protection of information systems in the face of ever-growing cyber threats. In the modern digital world, where cyberattacks are becoming increasingly complex and widespread, the use of artificial intelligence in cybersecurity is extremely relevant. The problem of cybersecurity is becoming increasingly important and conducting relevant research is becoming a necessary aspect of ensuring security in the digital space.In this context, we propose an article that examines the role of artificial intelligence, innovations, challenges, and prospects of its development in the sphere of cybersecurity. Additionally, it explores the utilization of artificial intelligence for automating processes of detecting, analysing, and responding to cyber threats, and its practical significance for enhancing security levels in the digital space and the effectiveness of cyber threat protection. The main approaches for this study are trend analysis and expert assessments, which allow for a comprehensive review of the directions of artificial intelligence development in the field of cybersecurity, the benefits and challenges of its implementation, as well as potential opportunities and threats to information security. The article discusses advanced security information and event management systems designed to collect, analyse and respond to cyber threats and other systems aimed at specific aspects ofprotection and expanding automation capabilities to improve the security level of information systems. The materials of the article are of theoretical value for further scientific research on the use of artificial intelligence in the sphere of cybersecurity and reflect important features of its influence on this area. This research can contribute not only to deepening the understanding of the theoretical aspects of the use of artificial intelligence in cybersecurity but also to identifying specific practical applications and innovations that will help improve protection against cyber threats in the future.

Digital Twin was introduced over a decade ago, as an innovative all-encompassing tool, with perceived benefits including real-time monitoring, simulation, optimisation and accurate forecasting. However, the theoretical framework and practical implementations of digital twin (DT) are yet to fully achieve this vision at scale. Although an increasing number of successful implementations exist in research and industrial works, sufficient implementation details are not publicly available, making it difficult to fully assess their components and effectiveness, to draw comparisons, identify successful solutions, share lessons, and thus to jointly advance and benefit from the DT methodology. This work first presents a review of relevant DT research and industrial works, focusing on the key DT features, current approaches in different domains, and successful DT implementations, to infer the key DT components and properties, and to identify current limitations and reasons behind the delay in the widespread implementation and adoption of digital twin. This work identifies that the major reasons for this delay are: the fact the DT is still a fast evolving concept; the lack of a universal DT reference framework, e.g. DT standards are scarce and still evolving; problem- and domain-dependence; security concerns over shared data; lack of DT performance metrics; and reliance of digital twin on other fast-evolving technologies. Advancements in machine learning, Internet of Things (IoT) and big data have led to significant improvements in DT features such as real-time monitoring and accurate forecasting. Despite this progress and individual company-based efforts, certain research and implementation gaps exist in the field, which have so far prevented the widespread adoption of the DT concept and technology; these gaps are also discussed in this work. Based on reviews of past work and the identified gaps, this work then defines a conceptualisation of DT which includes its components and properties; these also validate the uniqueness of DT as a concept, when compared to similar concepts such as simulation, autonomous systems and optimisation. Real-life case studies are used to showcase the application of the conceptualisation. This work discusses the state-of-the-art in DT, addresses relevant and timely DT questions, and identifies novel research questions, thus contributing to a better understanding of the DT paradigm and advancing the theory and practice of DT and its allied technologies.

The energy domain worldwide is experiencing high transformative pressure due to the imperative of climate change and new opportunities brought by various rapidly evolving digital technologies. Particularly, this change is affecting smart grids (SGs), which are increasingly shifting toward utilizing renewable and distributed energy sources. The natural intermittency of these energy sources increases the complexity of SG operations, such as ensuring continuity of energy supply and demand response balancing. In mitigation of these challenges, the tools and complex approaches that digitalization can provide have shown themselves particularly advantageous. There is a solid body of work showing how technologies like the Internet of Things (IoT), distributed ledgers, edge and cloud computing, machine learning (ML), etc., can be applied to address a variety of technical and economic problems in the energy sector, emphasizing SGs. This paper presents the most comprehensive literature review to date on digitalization in renewable energy source-based SGs, synthesizing over 200 studies across data analytics, artificial intelligence and ML, digital twins, edge–fog–cloud computing, the IoT, advanced metering infrastructure, and distributed ledger technologies. Unlike previous reviews, which are often limited to a single technology or narrow application, this work provides a cross-technology synthesis linking technical and financial aspects, identifies consolidated research gaps, and proposes a unified research agenda. The review further highlights future trends, including large language models, 6G communications, and distributed autonomous organizations, and discusses their implications for both industry practice and academic research.

The past years have witnessed an increasing interest and concern regarding the development of security monitoring and management mechanisms for Critical Infrastructures, due to their vital role in ensuring the availability of many essential services. This task is not easy due to the specific characteristics of such systems, and the natural resistance of Critical Infrastructures operators against actions implying downtime. Digital Twins, as accurate virtual models of physical objects or processes, can provide a faithful environment for security analysis or evaluation of potential mitigation strategies to be deployed in face of specific situations. Nonetheless, their on-premises deployment can be expensive, implying a significant CAPEX whose return will depend on the ability to plan and deploy a suitable support infrastructure, as well as implementing efficient and scalable data collection and processing mechanisms capable of taking advantage of the acquired resources. This paper presents an off-premises approach to design and deploy Digital Twins to secure critical infrastructures, developed in the scope of the ELEGANT project. Such Digital Twins are built using real-time, high fidelity replicas of Programming Logic Controllers, coupled with scalable and efficient data collection processes, supporting the development and validation of Machine Learning models to mitigate security threats like Denial of Service attacks. The validation approach of ELEGANT, which leveraged from the capabilities of the Fed4Fire federated testbeds evaluated the feasibility of using cloudified Digital Twins, thus converting a significant part of the projected CAPEX for the in-premises model into on-demand, pay-as-you-go OPEX, eventually paving the way for the establishment of a DTaaS (Digital Twin as a Service) paradigm. The achieved results demonstrate that the data pipelines providing support for the ELEGANT Digital Twins have low impact in terms of resource usage in Denial of Service and Distributed Denial of Service attack scenarios, when higher volumes of data are generated.

Aiming at the increasingly complex management and dispatching problems of power system, this paper combines Beidou precise space-time technology with digital twin system theory, and puts forward a method of constructing intelligent management and dispatching platform of power grid. The method combines Beidou and IoT (Internet of Things) sensors to form a digital twin management and scheduling platform which can perform state prediction, fault diagnosis, self-healing and other functions. Firstly, this paper analyzes the overall architecture of Beidou digital power twin system, then discusses the construction method of digital twin in power system from the aspects of digital twin modeling theory, data acquisition intelligent terminal based on Beidou, operation mode of digital twin platform, etc. Finally, the application test analysis is carried out. The research results show that the method proposed in this paper has reference value for improving the management and dispatching capability of smart grid.

This chapter aims to explore the managerial aspects of implementing artificial intelligence (AI) governance and digital twins (DTs) within the context of Industrial Internet of Things (IIoT). It focuses on establishing ethical and secure cybersecurity frameworks to enhance operational efficiency and protect critical infrastructure. The focus is on conducting systematic literature review. It begins with an overview of AI governance principles and the role of DTs in IIoT. The research then delves into the challenges and best practices for integrating these technologies, emphasising the importance of ethical considerations and robust cybersecurity measures. The study reveals that effective AI governance and the use of DTs can significantly improve the security and operational performance of IIoT systems. However, the integration of these technologies requires careful planning and adherence to ethical standards. For managers in the industrial sector, this chapter provides actionable insights into developing and implementing ethical and secure AI governance frameworks. It highlights the importance of fostering a culture of cybersecurity awareness and ethical responsibility, which are essential for safeguarding DTs and IIoT systems against cyber threats. This chapter contributes to the emerging discourse on AI governance and DTs by offering a managerial perspective on the ethical and secure deployment of these technologies in IIoT. It fills a gap in existing literature by providing a comprehensive analysis of the interplay between AI, DTs, and cybersecurity, and offering practical recommendations for industry leaders.

Additive manufacturing is a promising manufacturing process with diverse applications, but ensuring the quality and reliability of the manufactured products is a key challenge. The digital twin has emerged as a technology solution to address this challenge, allowing real-time monitoring and control of the manufacturing process. This paper proposes a digital twin system framework for additive manufacturing that integrates machine learning models, employing Unity, OctoPrint, and Raspberry Pi for real-time control and monitoring. Particularly, the system utilizes machine learning models for defect detection, achieving an Average Precision (AP) score of 92%, with specific performance metrics of 91% for defected objects and 94% for non-defected objects, demonstrating high efficiency. The Unity client user interface is also developed for control and visualization, facilitating easy additive manufacturing process monitoring. This research article presents a detailed description of the proposed digital twin framework and its workflow for implementation, the machine learning models, and the Unity client user interface. It also demonstrates the effectiveness of the integrated system through case studies and experimental results. The main findings show that the proposed digital twin system met its functional requirements and effectively detects defects and provides real-time control and monitoring of the additive manufacturing process. This paper contributes to the growing field of digital twin technology and additive manufacturing, providing a promising solution for enhancing the quality and reliability in the field of additive manufacturing.

Condition monitoring has come to the forefront of intelligent manufacturing and is particularly important in Computer Numerical Control (CNC) machining processes, where reliability, precision, and productivity are crucial. The traditional methods of monitoring, which are mostly premised on single sensors, the localized capture of data, and offline interpretation, are proving too small to handle current machining processes. Being limited in their scale, having limited computational power, and not being responsive in real-time, they do not fit well in a dynamic and data-intensive production environment. Recent progress in the Industrial Internet of Things (IIoT), cloud computing, and edge intelligence has led to a push into distributed monitoring architectures capable of obtaining, processing, and interpreting large amounts of heterogeneous machining data. Such innovations have facilitated more adaptive decision-making approaches, which have helped in supporting predictive maintenance, enhancing machining stability, tool lifespan, and data-driven optimization in manufacturing businesses. A structured literature search was conducted across major scientific databases, and eligible studies were synthesized qualitatively. This systematic review synthesizes over 180 peer-reviewed studies found in major scientific databases, using specific inclusion criteria and a PRISMA-guided screening process. It provides a comprehensive look at sensor technologies, data acquisition systems, cloud–edge–IoT frameworks, and digital twin implementations from an architectural perspective. At the same time, it identifies ongoing challenges related to industrial scalability, standardization, and the maturity of deployment. The combination of cloud platforms and edge intelligence is of particular interest, with emphasis placed on how the two ensure a balance in the computational load and latency, and improve system reliability. The review is a synthesis of the major advances associated with sensor technologies, data collection approaches, machine operations, machine learning, deep learning methods, and digital twins. The paper concludes with what can and cannot be performed to date by providing a comparative analysis of what is known about this topic and the reported industrial case applications. The main issues, such as the inconsistency of data, the lack of standardization, cyber threats, and old system integration, are critically analyzed. Lastly, new research directions are touched upon, including hybrid cloud–edge intelligence, advanced AI models, and adaptive multisensory fusion, which is oriented to autonomous and self-evolving CNC monitoring systems in line with the Industry 4.0 and Industry 5.0 paradigms. The review process was made transparent and repeatable by using a PRISMA-guided approach to qualitative synthesis and literature screening.

Threat actors can be persistent, motivated and agile, and leverage a\ndiversified and extensive set of tactics and techniques to attain their goals.\nIn response to that, defenders establish threat intelligence programs to stay\nthreat-informed and lower risk. Actionable threat intelligence is integrated\ninto security information and event management systems (SIEM) or is accessed\nvia more dedicated tools like threat intelligence platforms. A threat\nintelligence platform gives access to contextual threat information by\naggregating, processing, correlating, and analyzing real-time data and\ninformation from multiple sources, and in many cases, it provides centralized\nanalysis and reporting of an organization's security events. Sysmon logs is a\ndata source that has received considerable attention for endpoint visibility.\nApproaches for threat detection using Sysmon have been proposed, mainly\nfocusing on search engine technologies like NoSQL database systems. This paper\ndemonstrates one of the many use cases of Sysmon and cyber threat intelligence.\nIn particular, we present a threat assessment system that relies on a cyber\nthreat intelligence ontology to automatically classify executed software into\ndifferent threat levels by analyzing Sysmon log streams. The presented system\nand approach augments cyber defensive capabilities through situational\nawareness, prediction, and automated courses of action.\n

Recent trends of digital twin technologies in the energy sector: A comprehensive review

A survey of Digital Twin techniques in smart manufacturing and management of energy applications

Digital Twins (DTs) offer a wide range of functionalities including retrospective analyses of past conditions, real-time monitoring and control, as well as the evaluation of future scenarios. These capabilities make DTs a valuable tool in the Public Transport (PT) domain. To enable the functionalities demanded by traffic planners and operators, DTs rely on the integration of aggregated and disaggregated data from real-world sources and simulations. Informative visualizations of these data sources within the street network, combined with 3D representations of the built environment, provide the necessary contextualization to facilitate decision-making. However, since DTs integrate this large set of different components, their development is typically time- and resource-intensive which represents a barrier for their adoption. To make DTs more widely accessible and promote their use in the PT field, we propose and implement a DT development pipeline that integrates real-world data collection and processing, simulation, and 3D visualization in a unified framework. The pipeline is based on open data and open source software to enable the implementation of PT DTs with low barriers while allowing for extensibility to other urban-related applications. We demonstrate the DT’s functionalities with a prototypical implementation for the region of Kista in Stockholm, and discuss the potential as well as the limitations of PT DTs for practical use cases from a conceptual perspective.